A novel approach for controlling DC motor speed using NARXnet based FOPID controller

The importance of neural networks in control systems has grown in recent years as a result of their learning and universal approximation capabilities. When the plant dynamics are complex, system recognition and controller design become particularly difficult. In this paper, we propose a technique for identifying the system dynamics and neural network based Fractional Order Proportional Integral Derivative (FOPID) controller design for separately excited DC motor. A category of Recurrent Neural Networks (RNNs) called Nonlinear Auto Regressive with eXogenous input networks (NARXnets) are used to recognize the plant dynamics. To verify the proposed method, a separately excited DC motor is considered as plant and Harris Hawks Optimization (HHO) algorithm tuned FOPID controller as the model controller. The motor and controller dynamics are identified using NARXnets. The simulation results demonstrate that the proposed controller is performing superior to the conventional FOPID/PID controllers. The step and load response analysis shows stable and robust performance of neural network based FOPID controller. In addition, the proposed method can also be used as an alternative technique to approximate FOPID controllers using neural networks.